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Zhong L, Yang S, Rong Y, Qian J, Zhou L, Li J, Sun Z. Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques. Plants (Basel) 2024; 13:831. [PMID: 38592865 PMCID: PMC10974069 DOI: 10.3390/plants13060831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 04/11/2024]
Abstract
The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.
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Affiliation(s)
- Liang Zhong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; (L.Z.)
- Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Shengjie Yang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; (L.Z.)
- Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Yicheng Rong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; (L.Z.)
- Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Jiawei Qian
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; (L.Z.)
- Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Lei Zhou
- Livestock Development and Promotion Center, Linyi 276037, China
| | - Jianlong Li
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; (L.Z.)
- Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Zhengguo Sun
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing 210095, China
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Zhong L, Yang S, Chu X, Sun Z, Li J. Inversion of heavy metal copper content in soil-wheat systems using hyperspectral techniques and enrichment characteristics. Sci Total Environ 2024; 907:168104. [PMID: 37884148 DOI: 10.1016/j.scitotenv.2023.168104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
The growing problem of heavy metal contamination in soil will seriously threaten the China's grain safety. The development of hyperspectral remote sensing technology provides the possibility to achieve rapid and non-destructive monitoring of soil heavy metal content. In this study, we used hyperspectral techniques and enrichment characteristics to explore the potential of wheat leaf spectral inversion for heavy metal copper (Cu) content in the soil-wheat system. First, we conducted potting experiments to plant wheat on soil contaminated with varying concentrations of the heavy metal Cu. Then, we analyzed the migration characteristics, correlation characteristics and enrichment characteristics of Cu in the soil-wheat system under different soil heavy metal Cu concentration treatments. Next, we analyzed the spectral and correlation features of wheat leaves, and explored the potential of wheat leaf spectra for the inversion of Cu content in full-band and eigen-band modeling. Finally, using the estimated Cu content of wheat leaves from the best inversion model, we further conducted inversions to obtain the Cu content and precision of the grain, stem, root, total soil, and soil-available states based on the enrichment characteristics. The results showed that: (1) The accumulation pattern was root > grain > leaf > stem when the soil Cu concentration was <200 mg kg-1, and root > leaf > stem > grain when the soil Cu concentration was >200 mg kg-1. (2) The correlation coefficients between the different analyzed elements of the soil-wheat system were high, and all of them reached a highly significant level (P < 0.01). This supports the use of wheat leaves to estimate the Cu contents of soil and different parts of wheat. (3) The best inversion accuracies were obtained by modeling second derivative (SD) spectra that were pre-processed by screening the characteristic bands. The modeled R2cv, RMSEcv,R2ev and RMSEev were 0.94, 2.72 mg kg-1, 0.91 and 3.64 mg kg-1, respectively. This indicates an excellent ability to estimate Cu content in wheat leaves. (4) Using the hyperspectral estimation of Cu content in wheat leaves and the grouped inversion of enrichment characteristics, the inversion accuracy was lower only for grains, and the R2cv and R2ev for stems and roots exceeded 0.90, those for total soil exceeded 0.85, and those for the soil available state exceeded 0.70. Therefore, it is possible to use the spectra of wheat leaves in combination with the inversion of enrichment characteristics to estimate the soil-wheat Cu content. This study provides guarantee and support for the detection of grain safety.
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Affiliation(s)
- Liang Zhong
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Shengjie Yang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Xueyuan Chu
- School of Physics, Nanjing University, Nanjing 210023, China
| | - Zhengguo Sun
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Jianlong Li
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China; Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China; School of Physics, Nanjing University, Nanjing 210023, China; College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing 210095, China.
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Zhang C, Xue Y. Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection. Sensors (Basel) 2023; 24:217. [PMID: 38203082 PMCID: PMC10781383 DOI: 10.3390/s24010217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants' biochemical traits have become feasible due to advances in multispectral imaging sensors in recent years. This study evaluated the field application of proximal multispectral imaging combined with feature selection and regressive analysis to estimate the biochemical pigment contents of poplar leaves. The combination of 6 spectral bands and 26 vegetation indices (VIs) derived from the multispectral bands was taken as the group of initial variables for regression modeling. Three variable selection algorithms, including the forward selection algorithm with correlation analysis (CORR), recursive feature elimination algorithm (RFE), and sequential forward selection algorithm (SFS), were explored as candidate methods for screening combinations of input variables from the 32 spectral-derived initial variables. Partial least square regression (PLSR) and nonlinear support vector machine regression (SVR) were both applied to estimate total chlorophyll content (Chla+b) and carotenoid content (Car) at the leaf scale. The results show that the nonlinear SVR prediction model based on optimal variable combinations, selected by SFS using multiple scatter correction (MSC) preprocessing data, achieved the best estimation accuracy and stable prediction performance for the leaf pigment content. The Chla+b and Car models developed using the optimal model had R2 and RMSE predictive statistics of 0.849 and 0.825 and 5.116 and 0.869, respectively. This study demonstrates the advantages of using a nonlinear SVR model combined with SFS variable selection to obtain a more reliable estimation model for leaf biochemical pigment content.
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Affiliation(s)
- Changsai Zhang
- School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Xue
- School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
- School of Computing and Mathematics, College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK
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Zhang S, Fei T, Chen Y, Yang J, Qu R, Xu J, Xiao X, Cheng X, Hu Z, Zheng X, Zhao D. Identifying cadmium and lead co-accumulation from living rice blade spectrum. Environ Pollut 2023; 338:122618. [PMID: 37757932 DOI: 10.1016/j.envpol.2023.122618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/16/2023] [Accepted: 09/24/2023] [Indexed: 09/29/2023]
Abstract
Neither cadmium (Cd) nor lead (Pb) is necessary for crop growth, but they both can accumulate in soil and crop tissues, resulting in land degradation and crop reduction. Few researchers have explored how to detect Cd-Pb co-accumulation in leaves using proximal sensing techniques, especially by low-cost, easy-to-use leaf clips that capture hyperspectral reflections at suitable foliar positions. In this study, a hyperspectral imager was employed to collect images of the rice canopy from a designed greenhouse experiment that included 16 pretreatments of Cd-Pb co-accumulation, followed by spectral extractions from 3 foliar positions: the blade root, the middle of the leaf, and the leaf apex. A support vector machine with leave-one-out cross-validation was performed to diagnose the contaminative levels based on the feature wavelengths selected by an improved successive projection algorithm. Partial least squares regression was used to predict Cd-Pb concentrations in rice blades. The results indicated that diagnostic accuracies were varied using spectra of different foliar positions. The blade root and leaf apex of rice blades were the optimal foliar position for detecting Cd and Pb contamination, respectively. At the optimal foliar positions, diagnostic accuracies exceeded 0.80 for distinguishing whether the rice is subject to Cd-Pb contamination. The Cd prediction performed 'very good' with a residual prediction deviation (RPD) of 2.21, a R2 of 0.79, and a root mean square error (RMSE)of 6.14, while that of Pb was 1.62, 0.61, and 186.54. Important wavelengths were identified at 659-694 nm and 667-694 nm to detect Cd and Pb contamination. In summary, our results verified the feasibility and clarified the optimal foliar positions of rice blades to detect Cd-Pb contamination. The wavelengths selecting have the great potential in the design of future leaf clips, and the optimal foliar position can provide suggestions to improve diagnostic performances in field applications.
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Affiliation(s)
- Shuangyin Zhang
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
| | - Teng Fei
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China.
| | - Yiyun Chen
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
| | - Jiaxin Yang
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China
| | - Ran Qu
- China Center for Satellite Application on Ecology and Environment Ministry of Ecology and Environment, Beijing, 100094, China
| | - Jian Xu
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
| | - Xiao Xiao
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
| | - Xuejun Cheng
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
| | - Zhongzheng Hu
- China Centre for Resources Satellite Data and Application, Beijing, 100094, China
| | - Xuedong Zheng
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
| | - Dengzhong Zhao
- Changjiang River Scientific Research Institute, Changjiang Water Resources Committee, Wuhan, 430010, China; Wuhan Center for Intelligent Drainage Engineering Technology Research, Wuhan, 430010, China
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. Sci Total Environ 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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Ryckewaert M, Héran D, Trani JP, Mas-Garcia S, Feilhes C, Prezman F, Serrano E, Bendoula R. Hyperspectral images of grapevine leaves including healthy leaves and leaves with biotic and abiotic symptoms. Sci Data 2023; 10:743. [PMID: 37884537 PMCID: PMC10603033 DOI: 10.1038/s41597-023-02642-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed.
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Affiliation(s)
- Maxime Ryckewaert
- ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France.
- Inria, LIRMM, University of Montpellier, CNRS, Montpellier, France.
| | - Daphné Héran
- ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Carole Feilhes
- IFV, 1920 Route de Lisle-sur-Tarn, 81310, Peyrole, France
| | - Fanny Prezman
- IFV, 1920 Route de Lisle-sur-Tarn, 81310, Peyrole, France
| | - Eric Serrano
- IFV, 1920 Route de Lisle-sur-Tarn, 81310, Peyrole, France
| | - Ryad Bendoula
- ITAP, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. Plant Phenomics 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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